Interpretive Summary: Salmonella are a leading cause of foodborne illness and are estimated to cause 1.3 million cases of foodborne illness resulting in 15,608 hospitalizations and 553 deaths per year in the United States. Eggs, chicken and turkey are leading sources of foodborne illness caused by Salmonella. However, freshly processed chickens are contaminated with low levels of Salmonella; typically, less than 30 cells per carcass. In contrast, the dose of Salmonella that causes illness in 50 percent of healthy men is about one million cells and depends on the strain of Salmonella ingested. Thus, for Salmonella to cause illness they must multiply from a low initial dose on chicken to a higher dose. Mathematical models that predict survival and growth of foodborne pathogens from a low and ecological dose in response to storage and handling conditions are valuable tools for assessing and managing food safety risks. Before the current research was undertaken there were insufficient data to develop and validate a model for predicting the survival and growth of Salmonella on chicken during cold storage. A mathematical model was successfully developed and validated in the current study that was found to provide accurate and unbiased predictions of the survival and growth of low and ecological dose of two important Salmonella serotypes (Typhimurium and Kentucky) on chicken skin stored at refrigeration temperatures. The model provides more accurate predictions of Salmonella survival and growth on chicken stored at low temperatures than previous models developed with high and non-ecological doses of Salmonella. The model will benefit both the chicken industry and consumers by helping to better identify safe and unsafe chicken and thus, maximize the public health benefit of chicken by ensuring both its consumption and safety.

Technical Abstract:
The minimum growth temperature for Salmonella is in the range of 6 to 8'C, which is within temperatures encountered during cold storage of poultry. The objective of this study was to investigate and model survival and growth of Salmonella on chicken skin during cold storage. Chicken skin was inoculated with a low initial dose of less than 1 log of a single strain of Salmonella Typhimurium DT104 (ATCC 700408) followed by storage at 4 to 12'C for 0 to 10 days. A general regression neural network (GRNN) model that predicted the log change of S. Typhimurium DT104 as a function of time and temperature was developed. Percentage of residuals in an acceptable prediction zone from -1 (‘fail-safe’) to 0.5 (‘fail-dangerous’) log was used to validate the GRNN model using a criterion of 70% acceptable predictions. Performance of the model for predicting dependent data (n = 163) was 85.3% acceptable predictions. The model was also evaluated for interpolation and for extrapolation to another serotype of Salmonella (i.e. Kentucky). Performance of the model for predicting independent data for interpolation (n = 77) was 84.4% acceptable predictions, whereas performance of the model for predicting independent data for extrapolation (n = 70) to serotype Kentucky was 87.1% acceptable predictions. Thus, the model was found to provide acceptable predictions for survival and growth of Salmonella Typhimurium and Kentucky on chicken skin during cold storage. Mathematical models that predict the behavior of microbial pathogens on food are valuable tools for assessing and managing food safety risks because they can provide valid predictions of pathogen behavior in food under storage and handling conditions that were not investigated but that are within the conditions investigated and modeled and thus, save time and money associated with performing microbiological tests on food.